As the need for high performance computing continuously increases, the traditional Von Neumann computer architecture becomes less efficient. In recent years, neuromorphic hardware systems built upon the conventional CPU, GPU, or FPGA [4] gained a great deal of attention from people in industry, government and academia. Such systems can potentially provide the capabilities of biological perception and cognitive information processing within a compact and energy-efficient platform [1], [2].
As a highly generalized and simplified abstract of a biological system, an artificial neural network usually uses a weight matrix to represent a set of synapses. Accordingly, the net inputs of a group or groups of neurons can be transformed into matrix-vector multiplication(s). Similar to biological systems, neural network algorithms are inherently adaptive to the environment and resilient to random noise, however, hardware realizations of neural networks require a large volume of memory and are associated with high hardware cost if built with digital circuits [2]. Algorithm enhancement can alleviate the situation but cannot fundamentally resolve it. Thus, more efficient hardware-level solutions are necessary.
The existence of the memristor was predicted in circuit theory about forty years ago [5]. In 2008, the physical realization of a memristor was firstly demonstrated by HP Labs through a TiO2 thin-film structure [6]. Afterwards, many memristive materials and devices have been rediscovered. Intrinsically, a memristor behaves similarly to a synapse: it can “remember” the total electric charge/flux ever to flow through it [8], [9]. Moreover, memristor-based memories can achieve a very high integration density of 100 Gbits/cm2, a few times higher than flash memory technologies [7]. These unique properties make it a promising device for massively-parallel, large-scale neuromorphic systems [10], [11].
For the purpose of succinct description, the present invention uses the terminology “memristor” to represent the category of “resistive memory device”. For the remainder of the patent description, references to “memristor” shall be regarded as referring to any “resistive memory device”.
Based on circuit theory, an ideal memristor with memristance M builds the relationship between the magnetic flux φ and electric charge q that passes through the device, that is, dφ=M·dq. Since the magnetic flux and the electric charge are time dependent parameters, the instantaneous memristance varies with time and reflects the historical profile of the excitations through the device.
When developing actual memristive devices, many materials have demonstrated memristive behavior in theory and/or experimentation via different mechanisms. In general, a certain energy (or threshold voltage) is required to enable a state change in a memristor. When the electrical excitation through a memristor is greater than the threshold voltage, e.g., |vin|>|vth|, the memristance value changes. Otherwise, the memristor behaves like a resistor.